Supression res mauvais test brutus

higher/compare_res.py

@@ -2,7 +2,7 @@ from utils import *
 
 if __name__ == "__main__":
 
-    #'''
+    '''
     files=[
         #"res/good_TF_tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
         #"res/good_TF_tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
@@ -16,7 +16,7 @@ if __name__ == "__main__":
             data = json.load(json_file)
             plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
             #plot_TF_influence(data['Log'], param_names=data['Param_names'])
-    #'''
+    '''
     ## Loss , Acc, Proba = f(epoch) ##
     #plot_compare(filenames=files, fig_name="res/compare")
 
@@ -76,11 +76,11 @@ if __name__ == "__main__":
     '''
 
     #Res print
-    '''
+    #'''
     nb_run=3
     accs = []
     times = []
-    files = ["res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-150epochs(dataug:0)-1in_it-%s.json"%str(run) for run in range(nb_run)]
+    files = ["res/brutus-tests/log/Aug_mod(Data_augV5(Mix1.0-18TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-1in_it-%s.json"%str(run) for run in range(nb_run)]
     
     for idx, file in enumerate(files):
         #legend+=str(idx)+'-'+file+'\n'
@@ -90,4 +90,4 @@ if __name__ == "__main__":
         times.append(data['Time'][0])
 
     print(files[0], np.mean(accs), np.std(accs), np.mean(times))
-    '''
+    #'''

higher/dataug.py

@@ -5,7 +5,7 @@ from torch.distributions import *
 
 #import kornia
 #import random
-#import numpy as np
+import numpy as np
 import copy
 
 import transformations as TF
@@ -692,7 +692,6 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
         else:
             return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param)
 
-import numpy as np
 class Data_augV6(nn.Module): #Optimisation sequentielle
     def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=None, fixed_mag=True, shared_mag=True):
         super(Data_augV6, self).__init__()

higher/test_dataug.py

@@ -96,7 +96,7 @@ if __name__ == "__main__":
     tf_dict = {k: TF.TF_dict[k] for k in tf_names}
     #tf_dict = TF.TF_dict
     #aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
-    aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
+    aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
     #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
     #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
     print(str(aug_model), 'on', device_name)

higher/train_utils.py

@@ -616,9 +616,10 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
                 model_copy(src=fmodel, dst=model)
                 optim_copy(dopt=diffopt, opt=inner_opt)
 
-                meta_opt.step()
-                model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
-                #model['data_aug'].next_TF_set()
+                if epoch>50:
+                    meta_opt.step()
+                    model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
+                    #model['data_aug'].next_TF_set()
 
                 fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
                 diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel, track_higher_grads=high_grad_track)